STRETCHing HIV treatment: A Replication Study of Task Shifting in South Africa

The Streamlining Tasks & Roles to Expand Treatment and Care for HIV (STRETCH) program was developed to increase the reach of antiretroviral therapy (ART) for HIV/AIDS patients in Sub-Saharan Africa by training nurses to prescribe, initiate, and maintain ART. Fairall and colleagues conducted a cluster-randomized trial to determine the effects/impact of STRETCH on patient health outcomes in South Africa between 2008 and 2010. The purpose of our replication study is to evaluate Fairall and colleagues\u27 findings. We conducted push button and pure replication studies and measurement and estimation analyses (MEA). Our MEA validates the original findings: (1) overall, time to death did not differ between intervention (STRETCH) and control (ART) patients; (2) in a subgroup analysis of patients with CD4 counts of 201-350 cells per μL, the intervention group patients had a 30% lower risk of death than those in the control group, when controlling for baseline characteristics; (3) in a subgroup analysis of patients with CD4 counts of ≤200 cells per μL, time to death did not differ between the two groups; and (4) rates of viral suppression one year after enrollment did not differ between the intervention and control groups. This set of results have more caveats in the MEA. Although the intervention did not lead to improvements in the main outcomes, the effectiveness of STRETCH was proven to be similar to standard care while increasing the pool of prescribers, expanding their geographical range, and improving the quality of care for patients. Therefore, our analyses support the implementation of task shifting of antiretroviral therapy from doctors to trained nurses, which enhances confidence in the implementation of the intervention program and policymaking not only in South Africa but also in other developing countries that have similar circumstances

Non-Homogeneous Markov Process Models with Incomplete Observations: Application to a Dementia Disease Study

Identifying risk factors for transition rates among normal cognition, mildly cognitive impairment, dementia and death in an Alzheimer\u27s disease study is very important. It is known that transition rates among these states are strongly time dependent. While Markov process models are often used to describe these disease progressions, the literature mainly focuses on time homogeneous processes, and limited tools are available for dealing with non-homogeneity. Further, patients may choose when they want to visit the clinics, which creates informative observations. In this paper, we develop methods to deal with non-homogeneous Markov processes through time scale transformation when observation times are pre-planned with some observations missing. Maximum likelihood estimation via the EM algorithm is derived for parameter estimation. Simulation studies demonstrate that the proposed method works well under a variety of situations. An application to the AD study identifies that there is a significant increase in transition rates as a function of time. Furthermore, our models reveal that the nonignorable missing mechanism is perhaps reasonable

Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the EM algorithm give consistent estimators for model parameters when data are missing at random provided that the response model and the missing covariate model are correctly specified; but we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation which gives consistent estimators if the missing data and response models are correctly specified; but we do not need to specify the distribution of the covariates that have missing values. In this paper we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are missing at random. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations

Statistical Methods for Multi-State Analysis of Incomplete Longitudinal Data

Analyses of longitudinal categorical data are typically based on semiparametric models in which covariate effects are expressed on marginal probabilities and estimation is carried out based on generalized estimating equations (GEE). Methods based on GEE are motivated in part by the lack of tractable models for clustered categorical data. However such marginal methods may not yield fully efficient estimates, nor consistent estimates when missing data are present. In the first part of the thesis I develop a Markov model for the analysis of longitudinal categorical data which facilitates modeling marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy properties such as optimal efficiency and consistency, and remain consistent when data are missing at random. Simulation studies demonstrate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects. Incomplete data often arise in many areas of research in practice. This phenomenon is common in longitudinal data on disease history of subjects. Progressive models provide a convenient framework for characterizing disease processes which arise, for example, when the state represents the degree of the irreversible damage incurred by the subject. Problems arise if the mechanism leading to the missing data is related to the response process. A naive analysis might lead to biased results and invalid inferences. The second part of this thesis begins with an investigation of progressive multi-state models for longitudinal studies with incomplete observations. Maximum likelihood estimation is carried out based on an EM algorithm, and variance estimation is provided using Louis method. In general, the maximum likelihood estimates are valid when the missing data mechanism is missing completely at random or missing at random. Here we provide likelihood based method in that the parameters are identifiable no matter what the missing data mechanism. Simulation studies demonstrate that the proposed method works well under a variety of situations. In practice, we often face data with missing values in both the response and the covariates, and sometimes there is some association between the missingness of the response and the covariate. The proper analysis of this type of data requires taking this correlation into consideration. The impact of attrition in longitudinal studies depends on the correlation between the missing response and missing covariate. Ignoring such correlation can bias the statistical inference. We have studied the proper method that incorporates the association between the missingness of the response and missing covariate through the use of inverse probability weighted generalized estimating equations. The simulation illustrates that the proposed method yields a consistent estimator, while the method that ignores the association yields an inconsistent estimator. Many analyses for longitudinal incomplete data focus on studying the impact of covariates on the mean responses. However, little attention has been directed to address the impact of missing covariates on the association parameters in clustered longitudinal studies. The last part of this thesis mainly addresses this problem. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters

Study on the Imprinting Status of Insulin-Like Growth Factor II (IGF-II) Gene in Villus during 6–10 Gestational Weeks

Objective. To compare the difference of imprinting status of insulin-like growth factor II (IGF-II) gene in villus between normal embryo development group and abnormal embryo development group and to investigate the relationship between karyotype and the imprinting status of IGF-II gene. Methods. A total of 85 pregnant women with singleton pregnancy were divided into two groups: one with abnormal embryo development (n = 38) and the other with normal embryo development (n = 47). Apa I polymorphism of IGF-II gene in chorionic villus was assayed with reverse transcriptase polymerase chain reaction (RT-PCR) and restriction fragment length polymorphism (RFLP). The relationship between chromosomal abnormal karyotype and IGF-II gene imprinting status was analyzed by primary cell culture and G-banding chromosomal karyotype analysis. Results. IGF-II imprinting loss rate was higher in the abnormal embryo development group than the normal embryo development group (44.7% versus 31.6%), but without significant difference (P > .05). The percentage of abnormal chromosomes of chorionic villus in the abnormal embryo development group was 42.5%, in which IGF-II imprinting loss rate reached 64.7%. No abnormal karyotypes were found in the normal embryo development group. However, there was significant difference in IGF-II imprinting loss rate between two groups (P > .05). Conclusion. During weeks 6–10 of gestation, abnormal embryonic development is correlated with chromosomal abnormalities. The imprinting status of IGF-II gene played important roles in embryonic development, and imprinting loss might be related to chromosomal abnormalities

Using a monotone single‐index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log‐log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single‐index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real‐data example is used to illustrate the proposed methods

Does Human Papillomavirus Affect Pregnancy Outcomes? An Analysis of Hospital Data 2012-2014

Objective: To estimate the rate of Human Papillomavirus among pregnant women and its impact on the pregnancy outcomes. Study design: This was a retrospective cohort study of women who sought prenatal care and later delivered at the Nebraska Medical Center from 2012-2014. Human Papillomavirus infection was based on a cytological cervicovaginal diagnosis (Pap test) report. Bivariate and multivariable analyzes were performed using SAS 9.3. Results: Of the total sample size of 4824 women, 221 (4.4%) were HPV-positive. Women with Human Papillomavirus infection had increased risk of preeclampsia (adjusted OR: 2.83 95% CI: 1.28-6.26) and were also 1.8 times more likely to deliver preterm compared to women with no Human Papillomavirus infection (adjusted OR: 1.8, 95% CI: 1.15-2.83). Additionally, Human Papillomavirus infection was found to be significantly associated with low birth weight (adjusted OR: 2.58; 95% CI: 1.56-4.27). Conclusions: Although the prevalence of Human Papillomavirus infection was relatively low in this sample, the study clearly indicated a positive association between Human Papillomavirus infection and adverse pregnancy outcomes. Further research is needed to understand the impact of Human Papillomavirus infection in a larger and diverse sample of women. Also, a closer follow-up of pregnant women affected by Human Papillomavirus infection may be warranted

DNMTs Play an Important Role in Maintaining the Pluripotency of Leukemia Inhibitory Factor-Dependent Embryonic Stem Cells.

Naive pluripotency can be maintained in medium with two inhibitors plus leukemia inhibitory factor (2i/LIF) supplementation, which primarily affects canonical WNT, FGF/ERK, and JAK/STAT3 signaling. However, whether one of these three supplements alone is sufficient to maintain naive self-renewal remains unclear. Here we show that LIF alone in medium is sufficient for adaptation of 2i/L-ESCs to embryonic stem cells (ESCs) in a hypermethylated state (L-ESCs). Global transcriptomic analysis shows that L-ESCs are close to 2i/L-ESCs and in a stable state between naive and primed pluripotency. Notably, our results demonstrate that DNA methyltransferases (DNMTs) play an important role in LIF-dependent mouse ESC adaptation and self-renewal. LIF-dependent ESC adaptation efficiency is significantly increased in serum treatment and reduced in Dnmt3a or Dnmt3l knockout ESCs. Importantly, unlike epiblast stem cells, L-ESCs contribute to somatic tissues and germ cells in chimeras. L-ESCs cultured under such simple conditions as in this study would provide a more conducive platform to clarify the molecular mechanism of ESCs in in vitro culture